Screen Unlocking by Spontaneous Flick Reactions with One-Class Classification Approaches. - Yoshitomo Matsubara; Haruhiko Nishimura; Toshiharu Samura; Hiroyuki Yoshimoto; Ryohei Tanimoto (ICMLA, 2016)

Distilled Split Deep Neural Networks for Edge-Assisted Real-Time Systems. - Yoshitomo Matsubara; Sabur Baidya; Davide Callegaro; Marco Levorato; Sameer Singh 001 (HotEdgeVideo@MobiCom, 2019)

COVIDLies: Detecting COVID-19 Misinformation on Social Media. - Tamanna Hossain; Robert L. Logan IV; Arjuna Ugarte; Yoshitomo Matsubara; Sean Young; Sameer Singh 001 (NLP4COVID@EMNLP, 2020)

Optimal Task Allocation for Time-Varying Edge Computing Systems with Split DNNs. - Davide Callegaro; Yoshitomo Matsubara; Marco Levorato (GLOBECOM, 2020)

Neural Compression and Filtering for Edge-assisted Real-time Object Detection in Challenged Networks. - Yoshitomo Matsubara; Marco Levorato (ICPR, 2020)

Reranking for Efficient Transformer-based Answer Selection. - Yoshitomo Matsubara; Thuy Vu; Alessandro Moschitti (SIGIR, 2020)

Head Network Distillation: Splitting Distilled Deep Neural Networks for Resource-Constrained Edge Computing Systems. - Yoshitomo Matsubara; Davide Callegaro; Sabur Baidya; Marco Levorato; Sameer Singh 001 (IEEE Access, 2020)

Split Computing for Complex Object Detectors: Challenges and Preliminary Results. - Yoshitomo Matsubara; Marco Levorato (CoRR, 2020)

torchdistill: A Modular, Configuration-Driven Framework for Knowledge Distillation. - Yoshitomo Matsubara (RRPR, 2021)

Ensemble Transformer for Efficient and Accurate Ranking Tasks: an Application to Question Answering Systems. - Yoshitomo Matsubara; Luca Soldaini; Eric Lind; Alessandro Moschitti (EMNLP (Findings), 2022)

Supervised Compression for Resource-Constrained Edge Computing Systems. - Yoshitomo Matsubara; Ruihan Yang; Marco Levorato; Stephan Mandt (WACV, 2022)

BottleFit: Learning Compressed Representations in Deep Neural Networks for Effective and Efficient Split Computing. - Yoshitomo Matsubara; Davide Callegaro; Sameer Singh 001; Marco Levorato; Francesco Restuccia 001 (WoWMoM, 2022)

SC2: Supervised Compression for Split Computing. - Yoshitomo Matsubara; Ruihan Yang; Marco Levorato; Stephan Mandt (CoRR, 2022)

Rethinking Symbolic Regression Datasets and Benchmarks for Scientific Discovery. - Yoshitomo Matsubara; Naoya Chiba; Ryo Igarashi 002; Tatsunori Taniai; Yoshitaka Ushiku (CoRR, 2022)

Towards Split Computing: Supervised Compression for Resource-Constrained Edge Computing Systems - Yoshitomo Matsubara (2022)

Cross-Lingual Knowledge Distillation for Answer Sentence Selection in Low-Resource Languages. - Shivanshu Gupta; Yoshitomo Matsubara; Ankit Chadha; Alessandro Moschitti (ACL (Findings), 2023)

SplitBeam: Effective and Efficient Beamforming in Wi-Fi Networks Through Split Computing. - Niloofar Bahadori; Yoshitomo Matsubara; Marco Levorato; Francesco Restuccia 001 (ICDCS, 2023)

torchdistill Meets Hugging Face Libraries for Reproducible, Coding-Free Deep Learning Studies: A Case Study on NLP. - Yoshitomo Matsubara (CoRR, 2023)

A Transformer Model for Symbolic Regression towards Scientific Discovery. - Florian Lalande; Yoshitomo Matsubara; Naoya Chiba; Tatsunori Taniai; Ryo Igarashi 002; Yoshitaka Ushiku (CoRR, 2023)

Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challenges. - Yoshitomo Matsubara; Marco Levorato; Francesco Restuccia 001 (ACM Comput. Surv., 2023)

SC2 Benchmark: Supervised Compression for Split Computing. - Yoshitomo Matsubara; Ruihan Yang; Marco Levorato; Stephan Mandt (Trans. Mach. Learn. Res., 2023)

A Multi-Task Supervised Compression Model for Split Computing. - Yoshitomo Matsubara; Matteo Mendula; Marco Levorato (WACV, 2025)